Statistical learning theory: a tutorial

Statistical learning theory: a tutorial

In this article, we provide a tutorial overview of some aspects of statistical learning theory, which also goes by other names such as statistical pattern recognition, nonparametric classification and estimation, and supervised learning. We focus on the problem of two-class pattern classification for various reasons. This problem is rich enough to capture many of the interesting aspects that ar...

Randomized Algorithms for Robust Controller Synthesis Using Statistical Learning Theory: A Tutorial Overview

By now it is known that several problems in the robustness analysis and synthesis of control systems are NP-complete or NP-hard. These negative results force us to modify our notion of `solvinga a given problem. An approach that is recently gaining popularity is that of using randomized algorithms, which can be used to solve a problem approximately, most of the time. We begin with the premise t...

A Statistical Theory of Active Learning

Active learning is a protocol for supervised machine learning, in which a learning algorithm sequentially requests the labels of selected data points from a large pool of unlabeled data. This contrasts with passive learning, where the labeled data are taken at random. The objective in active learning is to produce a highly-accurate classifier, ideally using fewer labels than the number of rando...

Statistical Learning Theory: A PAC-Bayesian Approach

Toward a Theory of Embodied Statistical Learning

The purpose of this paper is to outline a new formulation of statistical learning that will be more useful and relevant to the field of robotics. The primary motivation for this new perspective is the mismatch between the form of data assumed by current statistical learning algorithms, and the form of data that is actually generated by robotic systems. Specifically, robotic systems generate a v...